PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma
PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma
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DOI：
10.1145/3447548.3467195
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发表时间：
2021-08
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影响因子：
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通讯作者：
Linchuan Xu;R. Asaoka;Taichi Kiwaki;Hiroshi Murata;Yuri Fujino;K. Yamanishi
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作者：
Linchuan Xu;R. Asaoka;Taichi Kiwaki;Hiroshi Murata;Yuri Fujino;K. Yamanishi
青光眼可对人眼视力造成不可逆的损害，传统上通过视野（VF）敏感性来诊断。然而，测量VF是劳动密集型和耗时的。最近，光学相干断层扫描（OCT）已经被采用来测量视网膜层厚度（RT）以用于辅助诊断，因为青光眼使RT发生结构变化并且获得RT的成本低得多。特别地，RT可以主要以两种方式辅助。一种是从RT估计VF，使得临床医生仅需要获得患者的RT，然后将其转换为VF用于诊断。另一种是利用过去的VF和RT来预测未来的VF，即，预测VF随时间的进展。这两个计算任务作为两个数据挖掘任务执行，因为目前还没有关于所涉及的计算的确切形式的知识。在本文中，我们研究了一个新的问题，这是两个数据挖掘任务的集成。其动机是，这两个数据挖掘任务都处理从RT域到VF域的信息转换，使得在一个任务中发现的知识可以对另一个任务有用。集成是不平凡的，因为这两个任务不共享转换方式。为了解决这个问题，我们设计了一个进度不可知和模式无关（PAMI）模块，促进跨任务的知识利用。我们的经验表明，我们提出的方法优于国家的最先进的方法的估计6.33%的均方根误差的平均值方面的真实的数据集，并优于国家的最先进的方法的进展预测的3.49%的最佳情况。
Glaucoma, which can cause irreversible damage to the sight of human eyes, is conventionally diagnosed by visual field (VF) sensitivity. However, it is labor-intensive and time-consuming to measure VF. Recently, optical coherence tomography (OCT) has been adopted to measure retinal layers thickness (RT) for assisting the diagnosis because glaucoma makes structural changes to RT and it is much less costly to obtain RT. In particular, RT can assist in mainly two manners. One is to estimate a VF from an RT such that clinical doctors only need to obtain an RT of a patient and then convert it to a VF for the diagnosis. The other is to predict future VFs by utilizing both past VFs and RTs, i.e., the prediction of progression of VF over time. The two computational tasks are performed as two data mining tasks because currently there is no knowledge about the exact form of the computations involved. In this paper, we study a novel problem which is the integration of the two data mining tasks. The motivation is that both the two data mining tasks deal with transforming information from the RT domain to the VF domain such that the knowledge discovered in one task can be useful for another. The integration is non-trivial because the two tasks do not share the way of transformation. To address this issue, we design a progression-agnostic and mode-independent (PAMI) module which facilitates cross-task knowledge utilization. We empirically demonstrate that our proposed method outperforms the state-of-the-art method for the estimation by 6.33% in terms of mean of the root mean square error on a real dataset, and outperforms the state-of-the-art method for the progression prediction by 3.49% for the best case.